About the Company: Electronic Arts Inc. is a global leader in interactive entertainment. We develop games, content and online services across platforms. We have a broad portfolio of brands that span the most popular genres.We exist to Inspire the World to Play. We create extraordinary new game experiences for our millions of players everywhere by bringing together experienced people that combine creativity, innovation, and passion. We celebrate diversity and inclusion and aim to create great experiences for our employees as often as our players.

Key Responsibilities: •  Design, build, and optimize scalable data acquisition, transformation, and integration pipelines to meet evolving business requirements. •  Develop, maintain, and automate ETL/ELT processes to extract, transform, and load data from diverse sources into analytical and operational systems with high accuracy and timeliness. •  Optimize data integration systems for scalability, performance, and reliability as data volume and complexity increase. •  Create and maintain logical and physical data models, ensuring efficient data storage, retrieval, and integration across platforms. •  Collaborate with cross-functional teams to define and implement data strategies, data flows, and conceptual models aligned with enterprise architecture standards. •  Ensure data quality, consistency, and governance by implementing validation, lineage, and monitoring frameworks. •  Analyze complex data sets to design business intelligence, analytics, and integration solutions. •  Translate business requirements into technical designs and scalable implementations using modern data engineering tools and practices. •  Develop and optimize complex SQL queries, stored procedures, and automation scripts using SQL, Python, and/or JavaScript. •  Leverage APIs and Python-based automation to streamline data workflows and improve processing efficiency. •  Apply prompt engineering and natural language processing (NLP) techniques to enable AI-driven data querying and insight generation. •  Integrate machine learning models into data pipelines where applicable to support predictive and prescriptive analytics use cases. •  Implement data observability and monitoring solutions to proactively identify and resolve issues. •  Follow best practices for programming, documentation, version control, and CI/CD in data integration projects. •  Develop and document data audit, archiving, and restoration procedures. •  Review and refine integration development efforts to ensure consistency, security, and adherence to best practices.